Lead-AI/ML

The Lead - AI/ML (Cybersecurity) will lead the design, development, and deployment of AI-driven solutions to improve cybersecurity efficiency, decision-making, and automation across the organization. The role focuses on identifying high-impact opportunities where AI, GenAI, and automation can reduce manual effort, enhance insight generation, and improve overall security outcomes through cross-team collaboration.

Key Responsibilities Strategy & Roadmap:

Lead the end-to-end design and deployment of AI/ML and GenAI solutions; define a roadmap aligned with enterprise security and technology objectives. Solution Development:

Apply Machine Learning (ML), Deep Learning (DL), GenAI, and

Agentic AI

to use cases such as security analysis, investigation assistance, and automated decision support. Lifecycle Management:

Oversee the full model lifecycle, including validation, monitoring, performance optimization, and drift management (MLOps). Security Integration:

Support the integration of AI solutions with existing platforms (SIEM, SOAR, VM) through APIs and microservices. Responsible AI:

Ensure all solutions adhere to secure, explainable, and responsible AI practices, specifically addressing

hallucination handling

and privacy requirements. Leadership & Influence:

Provide technical guidance to engineers and data scientists; communicate AI insights and limitations to senior stakeholders in business-relevant terms.

Technical Knowledge & Tooling AI / Machine Learning / GenAI Core AI:

Supervised/unsupervised learning, anomaly detection, and NLP. Advanced GenAI:

Deep understanding of

LLMs, Agentic AI patterns, and AI orchestration frameworks . Emerging Tech:

Familiarity with

Model Context Protocol (MCP)

style integrations and modern hallucination-handling techniques. Operations:

Awareness of MLOps, model explainability, and trust/ethics principles.

Cybersecurity Domain (Conceptual) Workflows:

Working knowledge of

SOC/Cyber Defense , Vulnerability Management, and Threat Intelligence. Tooling:

Familiarity with SIEM, SOAR, and industry frameworks like

MITRE ATT&CK .

Technology Stack (Indicative) Programming:

Python or similar languages for data/AI workloads. AI-Assisted Dev:

Experience using

Claude, Cursor, or GitHub Copilot

to accelerate prototyping and research. Infrastructure:

Cloud platforms (AWS/Azure/GCP), SQL/NoSQL databases, and Data Lakes.

Experience & Qualifications Experience:

8–12 years of total experience with a strong track record in AI/ML, data, or advanced analytics. Production Success:

Demonstrated experience delivering AI-enabled solutions in a live enterprise production environment. Leadership:

Prior experience leading initiatives, mentoring team members, or influencing technical direction. Education:

Bachelor’s or Master’s degree in Computer Science, Data Science, AI, or Cybersecurity. Certifications:

AI/ML or Cybersecurity certifications are a plus but not mandatory.